Future Apple devices may recognize your face
Baku, January 6 (AZERTAC). Future OS X and iOS devices may recognize your face when you look at them, if an Apple patent (number 20110317872) for "low threshold face recognition" comes to fruition.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are disclosed for reducing the impact of lighting conditions and biometric distortions, while providing a low-computation solution for reasonably effective (low threshold) face recognition. In one aspect, the methods include processing a captured image of a face of a user seeking to access a resource by conforming a subset of the captured face image to a reference model.
The reference model corresponds to a high information portion of human faces. The methods further include comparing the processed captured image to at least one target profile corresponding to a user associated with the resource, and selectively recognizing the user seeking access to the resource based on a result of said comparing. Robert Mikio Free is the inventor.
Here`s Apple`s background and summary of the invention: "This specification relates to low threshold face recognition, e.g., a face recognition system that can tolerate a certain level of false positives in making face recognition determinations. Most face recognition systems fall into one of two categories. A first category system tends to be robust and can tackle various lighting conditions, orientations, scale and the like, and tends to be computationally expensive.
"A second category system is specialized for security-type applications and can work under controlled lighting conditions. Adopting the first category systems for face recognition on consumer operated portable appliances that are equipped with a camera would unnecessarily use an appliance`s computing resources and drain its power. Moreover, as the consumer portable appliances tend to be used both indoor and outdoor, the second category systems for face recognition may be ineffective.
"Such ineffectiveness may be further exacerbated by the proximity of the user to the camera, i.e., small changes in distance to and tilt of the appliance`s camera dramatically distort features, making traditional biometrics used in security-type face recognition ineffective.
"This specification describes technologies relating to reducing the impact of lighting conditions and biometric distortions, while providing a low-computation solution for reasonably effective (low threshold) face recognition that can be implemented on camera-equipped consumer portable appliances.
"In general, one aspect of the subject matter described in this specification can be implemented in methods performed by an image processor that include the actions of processing a captured image of a face of a user seeking to access a resource by conforming a subset of the captured face image to a reference model. The reference model corresponds to a high information portion of human faces.
"The methods further include comparing the processed captured image to at least one target profile corresponding to a user associated with the resource, and selectively recognizing the user seeking access to the resource based on a result of said comparing.
"These and other implementations can include one or more of the following features. In some cases, the high information portion includes eyes and a mouth. In some other cases, the high information portion further includes a tip of a nose. Processing the captured image can include detecting a face within the captured image by identifying the eyes in an upper one third of the captured image and the mouth in the lower third of the captured image.
"The reference model includes a reference image of a face, and processing the captured image further can include matching the eyes of the detected face with eyes of the face in the reference image to obtain a normalized image of the detected face. Additionally, processing the captured image can further include vertically scaling a distance between an eyes-line and the mouth of the detected face to equal a corresponding distance for the face in the reference image in order to obtain the normalized image of the detected face. In addition, processing the captured image can further include matching the mouth of the detected face to the mouth of the face in the reference image in order to obtain the normalized image of the detected face”.
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